The baseline wandering noise removal from ECG signal using forward–backward Riemann Liouville fractional integral-based empirical wavelet transform approach

Abstract

The electrocardiogram (ECG) non-invasively monitors the electrical activities of the heart to diagnose the heart-related diseases. The baseline wandering noise affects the diagnosis of the heart diseases. In this paper, the baseline wandering noise removal is done using forward–backward Riemann Liouville (RL) fractional integral-based empirical wavelet transform (EWT) approach. In the designed methodology, firstly, the noisy ECG signal is decomposed into various modes from low to high frequencies. Then, the first mode is processed to remove the baseline wandering noise. The processed EWT mode is filtered by the fractional RL filter used in the forward direction and then in the backward direction for removing the baseline wandering noise from the ECG signal. After that, the processed and the unprocessed modes are used to reconstruct the denoised ECG signal. The clean ECG signal record is taken from MIT-BIH ECG-ID database, and the baseline wandering noise record is taken from the MIT-BIH noise stress test database. The performance of the proposed approach is validated in terms of the output signal-to-noise ratio (SNR[Formula: see text]). The comparative study has also been done between the proposed denoising approach and the existing state-of-the-art denoising algorithms. The experimental result proves the supremacy of our proposed denoising approach.